BIGS is a team labeled by INRIA, by CNRS and by University Henri Poincaré, via the Institut Élie Cartan of Nancy (UMR 7502 CNRS-INRIA-UHP-INPL-University of Nancy 2). Our research is mainly focused on statistics and stochastic processes techniques aiming at a better understanding of biological systems. A special attention is devoted to online data analysis, local regression techniques and identification of complex biological systems. Our investigations encompass both theoretical aspects and concrete applications of the issues alluded to above. To be more specific, we focus on the following topics:

*Online Factorial Analysis:*High dimensional data are often obtained online, and cannot be stored integrally in a computer memory. One of the recent challenges in data analysis is then to
be able to perform an accurate classification or clustering by taking advantage of the possibility of updating the information. This has to be done, of course, in a rather simple and efficient
way, allowing real time analysis. To this aim, we use techniques based on some sophisticated tools coming from stochastic approximation.

*Local Regression Techniques:*The main issue here is the construction of a procedure allowing to assess, in quite a general framework, whether a given model fits a data set regarding most
assumptions made in elaborating the model. This is based on a generalization of the Cramer-Von Mises statistics and involves a non parametric estimate of the conditional distribution of the
response variable. A detailed analysis of the procedure, including rate of convergence and asymptotic properties, is being performed. The strategy is then implemented for a study concerning
fetal biometry.

*Photodynamic therapy:*Since 1988, some control system scientists and biologists at the Centre de Recherche en Automatique de Nancy (CRAN in short) have worked together to develop the
photodynamic therapy (PDT), an alternative treatment for cancer, by means of a model-based approach. The global aim in this direction is to use statistical as well as mechanistic models in
order to (i) improve the response reproducibility, (ii) help biologists and chemists in the design of new photosensitizing agents and (iii) provide insight into complex phenomena associated
with oncogenesis, tumor angiogenesis and interactions with the treatment. This heavily relies on the production of accurate and simple enough models involving various type of stochastic
processes, such as Markov chains, branching processes and stochastic differential equations. The main questions here concern generally identification or estimation properties, but simulation
issues can be important too.

*Estimation for complex biological systems:*Numerous biological systems are accurately described by multidimensional noisy differential equations driven by Gaussian processes (beyond the
realm of Brownian motion) or by fractional fields, for which asymptotic properties and parameter estimation are fruitful informations. We are thus be interested in studying this kind of
systems, having in mind 3 specific applications of interest for us: (i) Bacteriophage systems (ii) Random fluctuation of nanoparticles. (iii) Automatic detection of osteoporosis.

For 2011 we stress the following noticeable events:

PhD defense of Aurélien Deya (supervisor: Samy Tindel).

PhD defense of Roukaya Keinj (supervisors: Thierry Bastogne and Pierre Vallois).

*Participants: J-M. Monnez, R. Bar, P. Vallois.*Generally speaking, there exists an overwhelming amount of articles dealing with the analysis of high dimensional data. Indeed, this is one
of the major challenges in statistics today, motivated by internet or biostatistics applications. Within this global picture, the problem of classification or dimension reduction of online data
can be traced back at least to a seminal paper by Mac Queen
, in which the

Our point of view on the topic relies on the so-called
*french data analysis school*, and more specifically on Factorial Analysis tools. In this context, it was then rapidly seen that stochastic approximation was an essential tool (see
Lebart's paper
), which allows to approximate eigenvectors in a stepwise manner. A
systematic study of Principal Component and Factorial Analysis has then been leaded by Monnez in the series of papers
,
,
, in which many aspects of convergences of online processes are analyzed
thanks to the stochastic approximation techniques.

*Participants: S. Ferrigno, A. Muller.*In the context where a response variable

Many assumptions must be made to reach it as a possible model. Some require much thinking, as for example, those related to the functional form of
*directional*, in the sense that they can detect departures from only one or a few aspects of a null model. For example, many tests have been proposed in the literature to assess the
validity of an entertained structural part

With these preliminaries in mind, let us observe that one quantity which embodies all the information about the joint behavior of

The (nonparametric) estimation of this function is thus of primary importance. To this aim, notice that modern estimators are usually based on the local polynomial approach, which has been recognized as superior to classical estimates based on the Nadaraya-Watson approach, and are as good as the recent versions based on spline and other methods. In some recent works , , we address the following questions:

Construction of a global test by means of Cramer-von Mises statistic.

Optimal bandwidth of the kernel used for approximation purposes.

In most biological contexts, mathematics turn out to be useful in producing accurate models with dual objectives: they should be simple enough and meaningful for the biologist on the one hand, and they should provide some insight on the biological phenomenon at stake on the other hand. We have focused on this kind of issue in various contexts that we shall summarize below.

*Photodynamic Therapy:*Photodynamic therapy induces a huge demand of interconnected mathematical systems, among which we have studied recently the following ones:

*Bacteriophage therapy:*Let us mention a starting collaboration between BIGS and the Genetics and Microbiology department at the Universitat Autònoma de Barcelona, on the modeling of
bacteriophage therapies. The main objective here is to describe how a certain family of benign viruses is able to weaken a bacterium induced disease, which naturally leads to the introduction
of a noisy predator-prey system of equations. It should be mentioned that some similar problems have been treated (in a rather informal way, invoking a linearization procedure) by Carletti in
. These tools cannot be applied directly to our system, and our methods
are based on concentration and large deviations techniques (on which we already had an expertise
,
) in order to combine convergence to equilibrium for the deterministic
system and deviations of the stochastic system. Notice that A. Muller is also working with A. Debussche and O. Radulescu on a related topic
, namely the convergence of a model of cellular biochemical
reactions.

*Gaussian signals:*Nature provides us with many examples of systems such that the observed signal has a given Hölder regularity, which does not correspond to the one we might expect from a
system driven by ordinary Brownian motion. This situation is commonly handled by noisy equations driven by Gaussian processes such as fractional Brownian motion or (in higher dimensions of the
parameter) fractional fields.

The basic aspects of differential equations driven by a fractional Brownian motion (fBm) and other Gaussian processes are now well understood, mainly thanks to the
so-called
*rough paths*tools
, but also invoking the Russo-Vallois integration techniques
. The specific issue of Volterra equations driven by fBm, which is
central for the subdiffusion within proteins problem, is addressed in
.

Fractional fields are very often used to model irregular phenomena which exhibit a scale invariance property, fractional Brownian motion being the historical fractional model. Nevertheless, its isotropy property is a serious drawback for instance in hydrology or in medecine (see ). Moreover, the fractional Brownian motion cannot be used to model some phenomena for which the regularity varies with time. Hence, many generalization (gaussian or not) of this model has been recently proposed, see for instance for some Gaussian locally self-similar fields, for some non-Gaussian models, for anisotropic models.

Our team has thus contributed , , , , and still contributes , , , , to this theoretical study: Hölder continuity, fractal dimensions, existence and uniqueness results for differential equations, study of the laws to quote a few examples. As we shall see below, this line of investigation also has some impact in terms of applications: we shall discuss how we plan to apply our results to osteoporosis on the one hand and to fluctuations within protein molecules on the other hand.

When one desires to confront theoretical probabilistic models with real data, statistical tools are obviously crucial. We have focused on two of them: parameter identifiability and parameter estimation.

The parameter estimation for a family of probability laws has a very long story in statistics, and we refer to
for an elegant overview of the topic. Moving to the references more
closely related to our specific projects, let us recall first that the mathematical description of photodynamic therapy can be split up into three parametric models : the uptake model
(pharmacokinetics of the photosensitizing drug into cancer cells), the photoreaction model and the tumor growth model. (i) Several papers have been reported for the application of system
identification techniques to pharmacokinetics modeling problems. But two issues were ignored in these previous works: presence of timing noise and identification from longitudinal data.
In
, we have proposed a bounded-error estimation algorithm based on
interval analysis to solve the parameter estimation problem while taking into consideration uncertainty on observation time instants. Statistical inference from longitudinal data based on mixed
effects models can be performed by the
*Monolix*software (
http://

A few words should be said about the existing literature on statistical inference for diffusion or related processes, a topic which will be at the heart of three of our projects (namely photodynamic and bacteriophage therapies, as well as fluctuations within molecules). The monograph is a good reference on the basic estimation techniques for diffusion processes. The problem of estimating diffusions observed at discrete times, of crucial importance for applications, has been addressed mainly since the mid 90s. The maximum likelihood techniques, which are also classical for parameter estimation, are well represented by the contributions .

Some attention has been paid recently to the estimation of the coefficients of fractional or multifractional Brownian motion according to a set of observations. Let us quote for instance the nice surveys , . On the other hand, the inference problem for diffusions driven by a fractional Brownian motion is still in its infancy. A good reference on the question is , dealing with some very particular families of equations, which do not cover the cases of interest for us.

Our expertise in data analysis and advanced statistics methods has given raise no a wide number of interdisciplinary collaborations. Among those, here are the most challenging at a scientific level:

*(i) Peanut allergy:*In the recent past, a direct application of factorial analysis techniques has been concerned with a study about allergic patients. This project was focusing on
allergies to peanut, and aimed at predicting the level of an allergic crisis according to some biological parameters. In this context, no rigorous discriminant analysis had been performed
before, and the article
has been considered as an achievement in this direction.

*(ii) Fetal pathology:*An ongoing work concerning local regression techniques is related to Fetal Biometry, an investigation line suggested by a collaboration between our team and the
*Centre de Placentologie et Foetopathologie de la Maternité Régionale de Nancy*, under the direction of Professor Bernard Foliguet. The methods involved in Fetal Biometry are usually based
on the comparison of some measured values with the predicted values derived from reference charts or equations in a normal population. However, it happens that maternal and pregnancy
characteristics have a significant influence on in-utero Fetal Biometry. We will thus produce some models allowing to construct customized fetal biometric size charts. In order to evaluate
them, classical and polynomial regression can be used, but they are not the most appropriate to the kind data we have to handle. Hence, we plan to use local regression estimation in order to
perform such an evaluation.

*(iii) Cohorts analysis:*Some medical teams in Nancy are faced with an overwhelming amount of data, for which a serious statistical assessment is needed. Among those let us mention the
*Stanislas*cohort handled at the
*Centre Alexis Vautrin*, which provides a huge amount of data potentially enabling a sharp identification of the biological characters involved in cardiovascular deceases. As in many
instances in Biostatistics, one is then faced with a very high dimensional data, from which we hope to extract a reduced number of significant variables allowing to predict the cardiovascular
risk accurately. Moreover, these characters should be meaningful to practitioners. The objective for us is thus to design an appropriate variable selection, plus a classification procedure in
this demanding context.

Let also mention the starting collaboration with the INSERM team of Pr. Jean-Louis Guéant and the INRIA team Orpailleur (particularly with Marie-Dominique Desvignes and Malika Smail). The goal of this collaboration is to extract biological markers for different diseases (cognitive decline; inflammatory intestinal diseases; liver cancer). To this aim, the INSERM team provides us with several data cohorts with a high number of variables and subjects. As in the Stanislas cohort, the objective for us is to design an appropriate variable selection, plus a classification procedure in this demanding context. This work has the originality to combine our own techniques with those developed by the Orpailleur team, based on symbolic tools. We hope that this experience will enrich both points of view and give raise to new methods of data analysis.

Our main application for this line of investigation is the photodynamic therapy developed by T. Bastogne. We shall also focus on bacteriophage therapies and subdiffusion within molecules.

*(i) Photodynamic therapy.*One of the main application we have in mind for our identification problems is to model photodynamic therapy. This promising cancer treatment involves selective
uptake and retention of a photosensitive drug in a tumor, followed by irradiation with light at an appropriate wavelength. Photosensitizers are photoactive compounds such as for instance
porphyrins and chlorins. The activated photosensitizer is thought to produce singlet oxygen at high doses and thereby to initiate apoptotic and necrotic death of tumor. Due to the lack of
response reproducibility, the complexity of interactions between physical, chemical and biological aspects and the high cost of experiments, there is a real demand in good mathematical and
physical models which might help to better control and understand PDT responses. We are particularly concerned with modeling the drug uptake into cancer cells, the photoreactions induced by
light exposition and tumor growth kinetics.

*(ii) Bacteriophage systems.*A collaboration between our team, the Mathematics and the Genetics and Microbiology Departments at the
*Universitat Autònoma de Barcelona*(UAB) is being set up, focusing on probabilistic aspects of bacteriophage therapies for animal diseases like hemorrhagic septicemia in cattle or atrophic
rhinitis in swine. This kind of therapy consists in inoculating a (benign) virus to animals in order to kill the bacteria known to be responsible of the disease. It was in use in the Soviet
Union until the 80s, and is now re-emerging, still at an experimental level, due to the progressive slowdown in antibiotic efficiency.

Within this context, our analysis of a noisy predator-prey competition modeling the treatment helps to calibrate and to understand better the behavior of the system in terms of fluctuations around an equilibrium. Note that our preliminary contacts with the Genetics and Microbiology Departments at UAB also open the way to a particle model in order to represent the couple bacteria/virus living on a surface.

*(iii) Subdiffusion into molecules.*Our purpose here is a better understanding of the phenomena observed in nanoscale Biophysics, as explained in the series of papers
. The technological advances in nanoscale technologies allow the
observation of single molecules, and thus the description of newly observed phenomenon. A typical example of this new kind of observation is given by the fluctuations in the folding of a
protein-enzyme compound called
*Fre*, which is involved in the DNA synthesis of the (canonical) bacterium
*E. Coli*.

More specifically, the paper advocates for modeling this folding fluctuations by means of a Volterra type equation driven by a fractional Brownian motion. This convincing model is based on some experimental and physical evidences, and have also been observed in a wide number of recent biological experiments. However, the model exhibited in also raises some unsolved questions: some stochastic equations appearing in the models are not properly defined and their long time behavior is still mysterious. The lack of a method in order to simulate and estimate coefficients of these equations on a solid mathematical ground should also be mentioned. This is the kind of topic we wish to address, for which a preliminary contact with S. Kou and N. Pillai (Princeton University, USA) has been established.

*(iv) Osteoporosis.*During the year 2010-2011, C. Lacaux has been visiting the MAP 5 (Paris Descartes University) laboratory and joined the ANR Project MATAIM (Modèles Anisotropes de
Textures. Applications à l'Imagerie Médicale). This project, which involves both mathematicians and practitioners, is in particular interested in the osteoporosis diagnostic. The paper
is a first step in the direction of modeling trabecular bone x-ray
images by some operator scaling fields. Actually the estimation of the matrix, which characterizes the anisotropy of the model, is crucial for practical purposes. Hermine Biermé (Paris
Descartes Univesity) and Céline Lacaux are working on this problem using quadratic variations. Once the problem of estimation is solved, they plan a comparison of the theoretical model with
real data provided by our Biologist colleagues of the MATAIM project. If the model corresponds to real data (as suggested in
), this approach may help for the diagnostic of osteoporosis: a
numerical study has to be performed in order to find the parameter value which characterizes osteoporosis.

We are currently considering the possibility to implement our Matlab algorithms into the Matlab toolbox
*Contsid*, developed by the System Identification team of the CRAN (
http://

Participants: H. Cardot, P. Cénac, O. Collignon, J-M. Monnez, P. Vallois.

In 2011, our contributions to data analysis in a Biological context are twofold:

At a theoretical level, we have kept on working on the so-called online data analysis alluded to at the
*Scientific Foundations*Section. Specifically, we have carried on the construction of a fast and recursive algorithm for clustering large data sets with the

At a practical level, our efforts have focused on an interesting study concerning peanuts allergy, for which our expertise in data analysis allows for a good prediction of allergy severity by means of rigorous methods.

Let us now describe more precisely our articles:

*(i) A fast and recursive algorithm for clustering large data sets with k-medians.*Clustering with fast algorithms large samples of high dimensional data is an important challenge in
computational statistics. Borrowing ideas from MacQueen
, who introduced a sequential version of the k-means algorithm, a new
class of recursive stochastic gradient algorithms designed for the k-medians loss criterion is proposed in
,
. By their recursive nature, these algorithms are very fast and well
adapted to deal with large samples of data that are allowed to arrive sequentially. It is proved that the stochastic gradient algorithm converges almost surely to the set of stationary points
of the underlying criterion. A particular attention is paid to the averaged versions, which are known to have better performances, and a data-driven procedure that allows automatic selection of
the value of the descent step is proposed. The performance of the averaged sequential estimator is compared on a simulation study, both in terms of computation speed and accuracy of the
estimations, with more classical partitioning techniques such as k-means, trimmed k-means and PAM (partitioning around medoids). Finally, this new on-line clustering technique is illustrated on
determining television audience profiles with a sample of more than 5000 individual television audience measured every minute over a period of 24 hours.

*(ii) Discriminant analyses of peanut allergy severity scores.*Peanut allergy is one of the most prevalent food allergies. The possibility of a lethal accidental exposure and the
persistence of the disease make it a public health problem. Evaluating the intensity of symptoms is accomplished with a double blind placebo-controlled food challenge (DBPCFC), which scores the
severity of reactions and measures the dose of peanut that elicits the first reaction. Since DBPCFC can result in life-threatening responses, we propose in
an alternate procedure with the long-term goal of replacing invasive
allergy tests. Discriminant analysis of DBPCFC score, the eliciting dose and the first accidental exposure score were performed in 76 allergic patients using 6 immunoassays and 28 skin prick
tests. A multiple factorial analysis was performed to assign equal weights to both groups of variables, and predictive models were built by cross-validation with linear discriminant analysis,
k-nearest neighbors, classification and regression trees, penalized support vector machine, stepwise logistic regression and Adaboost methods. We developed an algorithm for simultaneously
clustering eliciting doses and selecting discriminant variables. Our main conclusion is that antibody measurements offer information on the allergy severity, especially those directed against
*rAra-h1*and
*rAra-h3*. Further independent validation of these results and the use of new predictors will help extend this study to clinical practices.

Participants: S. Ferrigno, M. Maumy, A. Muller.

Consider

In order to explain the relationship between the variable of interest

In order to go one step further in this direction, we have chosen to work with another function. Namely, we study the conditional distribution function

At a more technical level, our study is based on a local linear nonparametric estimator of the conditional distribution function instead of the widely spread Nadaraya-Watson estimator. Indeed, it is a well-known fact that the asymptotic bias of the Nadaraya-Watson estimator behaves somehow badly. Observe however that local polynomial techniques are good alternatives. Based on these techniques, here are the steps we have focused on in 2010-2011 :

Our main result is the uniform law of the logarithm concerning the local linear estimator of the conditional distribution function (see ). We investigate convergence in probability and almost sure convergence results.

The uniform law of the logarithm has then been used to construct uniform asymptotic certainty bands for the conditional distribution function.

The certainty bands alluded to above have been applied to simulated data.

A variant of the test has been introduced in .

Let us also mention that applications of these theoretical results to survival analysis are currently the object of active research.

Participants: T. Bastogne, R. Keinj, P. Vallois.

Our research in this direction includes two contributions in 2011:

A multinomial model for cell growth allowing to calibrate radiotherapies given in .

A study of tumor growth based on the lifespan of each cell (see ).

More specifically, our two contributions can be summarized as follows:

*(i)*Hit and target models of tumor growth typically assume that all surviving cells have a constant and homogeneous sensitivity during the radiotherapy period. In
, we propose a multinomial model based on a discrete-time Markov chain,
able to take into account cell repair, cell damage heterogeneity and cell proliferation. The proposed model relies on the 'Hit paradigm' and 'Target' theory in radiobiology and assumes that a
cancer cell contains

Participants: X. Bardina, D. Bascompte, C. Rovira, S. Tindel.

In the last years Bacteriophage therapies are attracting the attention of several scientific studies. They can be a new and powerful tool to treat bacterial infections or to prevent them applying the treatment to animals such as poultry or swine. Very roughly speaking, they consist in inoculating a (benign) virus in order to kill the bacteria known to be responsible of a certain disease. This kind of treatment is known since the beginning of the 20th century, but has been in disuse in the Western world, erased by antibiotic therapies. However, a small activity in this domain has survived in the USSR, and it is now re-emerging (at least at an experimental level). Among the reasons of this re-emersion we can find the progressive slowdown in antibiotic efficiency (antibiotic resistance). Reported recent experiments include animal diseases like hemorrhagic septicemia in cattle or atrophic rhinitis in swine, and a need for suitable mathematical models is now expressed by the community.

Let us be a little more specific about the (lytic) bacteriophage mechanism: after attachment, the virus' genetic material penetrates into the bacteria and use the host's replication mechanism to self-replicate. Once this is done, the bacteria is completely spoiled while new viruses are released, ready to attack other bacteria. It should be noticed at this point that among the advantages expected from the therapy is the fact that it focuses on one specific bacteria, while antibiotics also attack autochthonous microbiota. Roughly speaking, it is also believed that viruses are likely to adapt themselves to mutations of their host bacteria.

At a mathematical level, whenever the mobility of the different biological actors is high enough, bacteriophage systems can be modeled by a kind of predator-prey
equation. Namely, set

where

With this model in hand, our main results in this direction (see ) have been the following:

Quantification of the exponential convergence to a bacteria-free equilibrium of equation (
) when

Use of the previous result plus concentration inequalities in order to study the convergence of the noisy system to equilibrium in a reasonable time range.

Simulation of the stochastic processes at stake in order to observe the convergence to equilibrium.

Participants: A. Crudu, A. Debussche, A. Muller, Aurélie, O. Radulescu.

We propose simplified models for the stochastic dynamics of gene network models arising in molecular biology. Those gene networks are classically modeled by Markov jump processes, which are extremely time consuming. To overcome this drawback, we study the asymptotic behavior of multiscale stochastic gene networks using weak limits of Markov jump processes.

We consider a set of chemical reactions

Mathematically, this evolution can be described by the following Markov jump process. It is based on a sequence

At time

In the applications we have in mind, the numbers of molecules have different scales. Some of the molecules are in small numbers and some are in large numbers. Accordingly, we split the set
of species into two sets

For this kind of system, we are able to give in
some relevant information on the asymptotic regime

Continuous piecewise deterministic processes (PDP) with switching.

PDP with jumps in the continuous variables.

Averaged PDP.

PDP with singular switching.

We justify rigorously the convergence for the four types of limits.

Participants: F. Baudoin, A. Chronopoulou, S. Cohen, F. Gamboa, Y. Hu, M. Jolis, C. Lacaux, J-M. Loubes, A. Neuenkirch, D. Nualart, C. Ouyang.

*(i) LAN property for fractional Brownian motion.*Local asymptotic normality (LAN) property is a fundamental concept in asymptotic statistics, which gives the asymptotic normality of
certain estimators such as the maximum likelihood estimator for instance (see
for details on this property). In
, we focus on the LAN property for the model where we observe a sample
of

with

*(ii) Inference for dynamical systems driven by Gaussian noises.*As mentioned at the
*Scientific Foundations*Section, the problem of estimating the coefficients of a general differential equation driven by a Gaussian process is still largely unsolved. To be more specific,
the most general (

where
*Application Domains*Section) require the analysis of the following

where

To this aim, here are the steps we have focused on in 2011:

A better understanding of the underlying rough path structure for equation ( ), carried out in , . This step allows a proper definition of our equation of interest in a wide range of contexts.

Gaussian type bounds for equations driven by a fractional Brownian motion, obtained in . This is an important preliminary step for likelihood estimates for stochastic processes.

Numerical aspects of a maximum likelihood type procedure for an equation of the form ( ), expressed in terms of Malliavin calculus tools (see ).

Convergence of a least square type estimator for an equation of the form (
) where the noise enters additively, handled in
. This is the first occurrence of a converging estimator for a
general coefficient

Participants: Hermine Biermé, Jacques Istas, Céline Lacaux, Renaud Marty, Hans-Peter Scheffler.

The Hölder regularity properties of operator scaling Gaussian or stable harmonizable random fields have been studied in
and can be expressed in terms of the matrix
*Application Domains*Section). In order to obtain some anisotropic random fields whose Hölder regularity properties are allowed to vary, we introduce in
a local version of the operator scaling property (similar to the local
version of the classical self-similarity property defined in
). This local property is illustrated in
, where we also define and study harmonizable multi-operator scaling
stable randoms fields. For such a multi-operator random field, we obtain an accurate upper bound of both the modulus of continuity and global and directional Hölder regularities at any point

for some suitable families

*Start-up project by T. Bastogne:*

Industrial partner: CyberBio (Biocybernetics for Cancerology & Nanomedicine).

Status: in incubation.

*CIFRE PhD grant supervised by P. Vallois:*

Industrial partner: Caisse Mutuelle du Crédit Agricole.

Title: Claim reserving for insurance.

PhD thesis of M. Geoffray Nichil.

*PEPS project (Mathematics-Industry Interactions) leaded by A. Muller:*

Industrial partner: Sport4Spirit (start-up).

Title: Computation of profit probabilities in sports gambling.

Two Internships involved.

*Co-direction of a PhD thesis by J-M. Monnez:*

Partner: Ecole de Hautes Etudes en Santé Publique (Nancy).

Title: Influence of socio-economic and environmental characteristics on infant mortality.

PhD thesis of M. Lalloué.

*Regional project leaded by T. Bastogne:*

Partners: Contrat de Projets Etat-Région, MISN (Modélisation, Information et Système Numérique), Thème AOC (Analyse, Optimisation et Contrôle).

Title: EMC2 (Experimental design, Modeling and Control in Cancerology).

Program: UGR (Université de la Grande Région)

Project acronym: I-DERBI

Project title: I-DERBI

Duration: January 2010 - April 2012

Coordinator: C. Carlberg (Luxembourg)

Other partners: Université du Luxembourg, Université de Liège (Belgium) , Saarland University (Germany)

Abstract: We stand at the brink of a fundamental change in how medicine will be practiced in the next 5-20 years. This change will require the unprecedented integration of biology, medicine, technology and computation as well as societal issues of major importance: ethical, regulatory, public policy, economic, and others. These needs have encouraged the emergence of a biology-based inter-disciplinary study field, systems biology, which focuses on the modeling of complex biological systems. Systems biology covers a large spectrum of applications: biomedicine, bioprocesses engineering, environmental science and pharmaceutical discovery. The ambition of the I-DERBI pilot project is to initiate and develop synergy of education and research in Systems Biology within the Grande Région.

Partner: Universitat Autònoma de Barcelona, Departament de Matemàtiques (Spain).

Subject: Stochastic model for bacteriophage systems.

Partner: TU Kaiserslautern, Department of Mathematics.

Subject: Parameter estimation for differential systems driven by Gaussian processes.

Yosra Chemli

Subject: Statistical Emulation of High Dimensional Biological Dynamic Models

Institution: Ecole Polytechnique de Tunisie (Tunisia)

Raouf Souabni

Subject: Simulation of the light propagation in biological tissues. Application to interstitial photodynamic therapy.

Institution: Université de Tunis El Manar - Faculté des Sciences (FST) (Tunisia)

BIGS is a team whose composition includes University staff only. All members teach numerous courses, ranging from L1 to M2 levels.

**PhD & HdR:**

PhD : A. Deya,
*Etude de systèmes différentiels fractionnaires*, Universté de Nancy 1, 18/10/2010, Advisor: S. Tindel.

PhD : R. Keijn,
*Modélisation de la croissance d'une tumeur après traitement par radiothérapie*, Universté de Nancy 1, 2/12/2011, Advisors: T. Bastogne, P. Vallois.

PhD in progress: R. Bar,
*Analyse de données en ligne*, from 01/09/2010. Advisor: J-M. Monnez.

PhD in progress: B. Lalloué:
*Analyse des données dans l'étude de l'influence de caractéristiques socio-spatiales sur des événements de santé*, from 01/09/2010. Advisor: J-M. Monnez.

PhD in progress: R. Bonidal:
*Analyse des systèmes discriminants multi-classes à grande marge*, from 01/09/2009. Advisors: Y. Guermeur, S. Tindel.

PhD in progress: G. Nichil,
*Claim reserving for insurance*, from 01/09/2010. Advisors: S. Herrmann (University of Dijon), P. Vallois.